Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations2768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory216.4 KiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Age is highly overall correlated with PregnanciesHigh correlation
Insulin is highly overall correlated with SkinThicknessHigh correlation
Pregnancies is highly overall correlated with AgeHigh correlation
SkinThickness is highly overall correlated with InsulinHigh correlation
Id is uniformly distributed Uniform
Id has unique values Unique
Pregnancies has 412 (14.9%) zeros Zeros
BloodPressure has 125 (4.5%) zeros Zeros
SkinThickness has 800 (28.9%) zeros Zeros
Insulin has 1330 (48.0%) zeros Zeros
BMI has 39 (1.4%) zeros Zeros

Reproduction

Analysis started2024-12-26 09:18:58.753949
Analysis finished2024-12-26 09:19:19.196455
Duration20.44 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Uniform  Unique 

Distinct2768
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1384.5
Minimum1
Maximum2768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2024-12-26T14:49:19.568048image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile139.35
Q1692.75
median1384.5
Q32076.25
95-th percentile2629.65
Maximum2768
Range2767
Interquartile range (IQR)1383.5

Descriptive statistics

Standard deviation799.1971
Coefficient of variation (CV)0.57724601
Kurtosis-1.2
Mean1384.5
Median Absolute Deviation (MAD)692
Skewness0
Sum3832296
Variance638716
MonotonicityStrictly increasing
2024-12-26T14:49:19.884911image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1850 1
 
< 0.1%
1842 1
 
< 0.1%
1843 1
 
< 0.1%
1844 1
 
< 0.1%
1845 1
 
< 0.1%
1846 1
 
< 0.1%
1847 1
 
< 0.1%
1848 1
 
< 0.1%
1849 1
 
< 0.1%
Other values (2758) 2758
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2768 1
< 0.1%
2767 1
< 0.1%
2766 1
< 0.1%
2765 1
< 0.1%
2764 1
< 0.1%
2763 1
< 0.1%
2762 1
< 0.1%
2761 1
< 0.1%
2760 1
< 0.1%
2759 1
< 0.1%

Pregnancies
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7427746
Minimum0
Maximum17
Zeros412
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2024-12-26T14:49:20.151271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.323801
Coefficient of variation (CV)0.88805802
Kurtosis0.33358506
Mean3.7427746
Median Absolute Deviation (MAD)2
Skewness0.95909632
Sum10360
Variance11.047653
MonotonicityNot monotonic
2024-12-26T14:49:20.427808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 491
17.7%
0 412
14.9%
2 387
14.0%
3 270
9.8%
4 259
9.4%
5 198
7.2%
6 181
 
6.5%
7 145
 
5.2%
8 134
 
4.8%
9 98
 
3.5%
Other values (7) 193
 
7.0%
ValueCountFrequency (%)
0 412
14.9%
1 491
17.7%
2 387
14.0%
3 270
9.8%
4 259
9.4%
5 198
7.2%
6 181
 
6.5%
7 145
 
5.2%
8 134
 
4.8%
9 98
 
3.5%
ValueCountFrequency (%)
17 4
 
0.1%
15 3
 
0.1%
14 9
 
0.3%
13 32
 
1.2%
12 32
 
1.2%
11 35
 
1.3%
10 78
2.8%
9 98
3.5%
8 134
4.8%
7 145
5.2%

Glucose
Real number (ℝ)

Distinct136
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.1026
Minimum0
Maximum199
Zeros18
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2024-12-26T14:49:20.747319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.35
Q199
median117
Q3141
95-th percentile181
Maximum199
Range199
Interquartile range (IQR)42

Descriptive statistics

Standard deviation32.036508
Coefficient of variation (CV)0.26454022
Kurtosis0.57911716
Mean121.1026
Median Absolute Deviation (MAD)20
Skewness0.16286439
Sum335212
Variance1026.3379
MonotonicityNot monotonic
2024-12-26T14:49:21.107098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 66
 
2.4%
100 61
 
2.2%
102 52
 
1.9%
129 51
 
1.8%
106 50
 
1.8%
95 49
 
1.8%
112 49
 
1.8%
105 47
 
1.7%
111 47
 
1.7%
108 46
 
1.7%
Other values (126) 2250
81.3%
ValueCountFrequency (%)
0 18
0.7%
44 3
 
0.1%
56 4
 
0.1%
57 7
 
0.3%
61 4
 
0.1%
62 3
 
0.1%
65 4
 
0.1%
67 3
 
0.1%
68 10
0.4%
71 13
0.5%
ValueCountFrequency (%)
199 4
 
0.1%
198 3
 
0.1%
197 12
0.4%
196 8
0.3%
195 10
0.4%
194 13
0.5%
193 8
0.3%
191 3
 
0.1%
190 4
 
0.1%
189 12
0.4%

BloodPressure
Real number (ℝ)

Zeros 

Distinct47
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.134393
Minimum0
Maximum122
Zeros125
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2024-12-26T14:49:21.444360image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q162
median72
Q380
95-th percentile90
Maximum122
Range122
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.231438
Coefficient of variation (CV)0.27817469
Kurtosis5.2750775
Mean69.134393
Median Absolute Deviation (MAD)8
Skewness-1.8504501
Sum191364
Variance369.84821
MonotonicityNot monotonic
2024-12-26T14:49:21.781533image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
70 201
 
7.3%
74 197
 
7.1%
78 173
 
6.2%
68 170
 
6.1%
64 163
 
5.9%
72 162
 
5.9%
80 138
 
5.0%
76 132
 
4.8%
60 129
 
4.7%
62 128
 
4.6%
Other values (37) 1175
42.4%
ValueCountFrequency (%)
0 125
4.5%
24 3
 
0.1%
30 5
 
0.2%
38 4
 
0.1%
40 3
 
0.1%
44 15
 
0.5%
46 8
 
0.3%
48 18
 
0.7%
50 44
 
1.6%
52 40
 
1.4%
ValueCountFrequency (%)
122 4
 
0.1%
114 4
 
0.1%
110 10
0.4%
108 7
0.3%
106 12
0.4%
104 7
0.3%
102 4
 
0.1%
100 12
0.4%
98 11
0.4%
96 12
0.4%

SkinThickness
Real number (ℝ)

High correlation  Zeros 

Distinct53
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.824422
Minimum0
Maximum110
Zeros800
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2024-12-26T14:49:22.111208image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile44
Maximum110
Range110
Interquartile range (IQR)32

Descriptive statistics

Standard deviation16.059596
Coefficient of variation (CV)0.77119047
Kurtosis-0.025897797
Mean20.824422
Median Absolute Deviation (MAD)12
Skewness0.18084092
Sum57642
Variance257.91061
MonotonicityNot monotonic
2024-12-26T14:49:22.433010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 800
28.9%
32 114
 
4.1%
30 102
 
3.7%
23 82
 
3.0%
27 81
 
2.9%
28 74
 
2.7%
18 74
 
2.7%
33 71
 
2.6%
39 70
 
2.5%
31 69
 
2.5%
Other values (43) 1231
44.5%
ValueCountFrequency (%)
0 800
28.9%
7 5
 
0.2%
8 8
 
0.3%
10 18
 
0.7%
11 20
 
0.7%
12 28
 
1.0%
13 41
 
1.5%
14 21
 
0.8%
15 47
 
1.7%
16 21
 
0.8%
ValueCountFrequency (%)
110 2
 
0.1%
99 3
 
0.1%
63 4
 
0.1%
60 3
 
0.1%
59 2
 
0.1%
56 4
 
0.1%
54 6
0.2%
52 6
0.2%
51 4
 
0.1%
50 10
0.4%

Insulin
Real number (ℝ)

High correlation  Zeros 

Distinct187
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.12789
Minimum0
Maximum846
Zeros1330
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2024-12-26T14:49:22.894211image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37
Q3130
95-th percentile293
Maximum846
Range846
Interquartile range (IQR)130

Descriptive statistics

Standard deviation112.30193
Coefficient of variation (CV)1.4015336
Kurtosis5.76048
Mean80.12789
Median Absolute Deviation (MAD)37
Skewness2.0781108
Sum221794
Variance12611.724
MonotonicityNot monotonic
2024-12-26T14:49:23.210201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1330
48.0%
105 42
 
1.5%
140 33
 
1.2%
130 31
 
1.1%
180 30
 
1.1%
120 29
 
1.0%
100 27
 
1.0%
94 24
 
0.9%
135 23
 
0.8%
76 22
 
0.8%
Other values (177) 1177
42.5%
ValueCountFrequency (%)
0 1330
48.0%
14 4
 
0.1%
15 4
 
0.1%
16 4
 
0.1%
18 7
 
0.3%
22 4
 
0.1%
23 6
 
0.2%
25 3
 
0.1%
29 4
 
0.1%
32 3
 
0.1%
ValueCountFrequency (%)
846 1
 
< 0.1%
744 3
0.1%
680 3
0.1%
600 3
0.1%
579 5
0.2%
545 3
0.1%
543 1
 
< 0.1%
540 4
0.1%
510 4
0.1%
495 7
0.3%

BMI
Real number (ℝ)

Zeros 

Distinct253
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.137392
Minimum0
Maximum80.6
Zeros39
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2024-12-26T14:49:23.626944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.8
Q127.3
median32.2
Q336.625
95-th percentile44.6
Maximum80.6
Range80.6
Interquartile range (IQR)9.325

Descriptive statistics

Standard deviation8.0761272
Coefficient of variation (CV)0.25130002
Kurtosis3.9233175
Mean32.137392
Median Absolute Deviation (MAD)4.7
Skewness-0.17657571
Sum88956.3
Variance65.223831
MonotonicityNot monotonic
2024-12-26T14:49:23.975155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 46
 
1.7%
31.2 45
 
1.6%
31.6 41
 
1.5%
0 39
 
1.4%
33.3 37
 
1.3%
32.4 35
 
1.3%
32.8 34
 
1.2%
30.8 33
 
1.2%
32.9 33
 
1.2%
30.1 31
 
1.1%
Other values (243) 2394
86.5%
ValueCountFrequency (%)
0 39
1.4%
18.2 11
 
0.4%
18.4 3
 
0.1%
19.1 3
 
0.1%
19.3 4
 
0.1%
19.4 3
 
0.1%
19.5 8
 
0.3%
19.6 9
 
0.3%
19.9 1
 
< 0.1%
20 4
 
0.1%
ValueCountFrequency (%)
80.6 2
 
0.1%
67.1 4
0.1%
64.4 2
 
0.1%
59.4 4
0.1%
57.3 4
0.1%
55 4
0.1%
53.2 4
0.1%
52.9 4
0.1%
52.7 2
 
0.1%
52.3 6
0.2%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct523
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47119256
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2024-12-26T14:49:24.412093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.141
Q10.244
median0.375
Q30.624
95-th percentile1.136
Maximum2.42
Range2.342
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.32566883
Coefficient of variation (CV)0.69115869
Kurtosis5.172935
Mean0.47119256
Median Absolute Deviation (MAD)0.168
Skewness1.8427907
Sum1304.261
Variance0.10606019
MonotonicityNot monotonic
2024-12-26T14:49:24.755504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258 22
 
0.8%
0.207 20
 
0.7%
0.261 18
 
0.7%
0.268 18
 
0.7%
0.238 18
 
0.7%
0.259 17
 
0.6%
0.284 16
 
0.6%
0.551 16
 
0.6%
0.52 16
 
0.6%
0.292 16
 
0.6%
Other values (513) 2591
93.6%
ValueCountFrequency (%)
0.078 3
 
0.1%
0.084 3
 
0.1%
0.085 7
0.3%
0.088 8
0.3%
0.089 3
 
0.1%
0.092 3
 
0.1%
0.096 4
0.1%
0.1 4
0.1%
0.101 3
 
0.1%
0.102 3
 
0.1%
ValueCountFrequency (%)
2.42 4
0.1%
2.329 3
0.1%
2.288 1
 
< 0.1%
2.137 4
0.1%
1.893 3
0.1%
1.781 3
0.1%
1.731 4
0.1%
1.699 3
0.1%
1.698 4
0.1%
1.6 4
0.1%

Age
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.132225
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2024-12-26T14:49:25.074239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q340
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.77723
Coefficient of variation (CV)0.35546148
Kurtosis0.77185874
Mean33.132225
Median Absolute Deviation (MAD)7
Skewness1.1662989
Sum91710
Variance138.70315
MonotonicityNot monotonic
2024-12-26T14:49:25.396030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 264
 
9.5%
21 229
 
8.3%
25 182
 
6.6%
24 168
 
6.1%
23 141
 
5.1%
28 133
 
4.8%
26 117
 
4.2%
27 113
 
4.1%
29 99
 
3.6%
31 82
 
3.0%
Other values (42) 1240
44.8%
ValueCountFrequency (%)
21 229
8.3%
22 264
9.5%
23 141
5.1%
24 168
6.1%
25 182
6.6%
26 117
4.2%
27 113
4.1%
28 133
4.8%
29 99
 
3.6%
30 77
 
2.8%
ValueCountFrequency (%)
81 4
 
0.1%
72 4
 
0.1%
70 4
 
0.1%
69 8
0.3%
68 4
 
0.1%
67 13
0.5%
66 16
0.6%
65 11
0.4%
64 4
 
0.1%
63 17
0.6%

Outcome
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
0
1816 
1
952 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1816
65.6%
1 952
34.4%

Length

2024-12-26T14:49:25.691250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-26T14:49:25.915400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1816
65.6%
1 952
34.4%

Most occurring characters

ValueCountFrequency (%)
0 1816
65.6%
1 952
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1816
65.6%
1 952
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1816
65.6%
1 952
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1816
65.6%
1 952
34.4%

Interactions

2024-12-26T14:49:16.492753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:48:59.480361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:01.542010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:03.676594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:05.819950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:08.112975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:10.144129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:12.108072image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:14.298271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:16.706623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:48:59.731358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:01.774868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:03.906454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:06.040352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:08.330055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:10.349103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:12.332933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:14.514137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:16.944475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:48:59.970999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:02.028680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:04.142040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:06.283908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:08.566907image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:10.581959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:12.578784image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:14.757098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:17.192324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:00.212523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:02.280141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:04.392888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:06.625695image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:08.807338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:10.812476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:12.829630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:14.995997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:17.415505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:00.438385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:02.522992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:04.625745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:06.989473image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:09.030201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:11.036815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:13.060739image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:15.221861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:17.626613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:00.651652image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:02.748852image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:04.859602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:07.208337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:09.240958image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:11.243256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:13.282548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:15.440570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:17.837483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:00.865844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:02.964748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:05.075821image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:07.421208image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:09.445832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:11.442483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:13.504411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:15.652229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:18.066949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:01.099035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:03.212596image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:05.343656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:07.659061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:09.690408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:11.673341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:13.747737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:15.899201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:18.291811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:01.322118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:03.442493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:05.576511image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:07.883087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:09.924264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:11.892207image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:13.978466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-26T14:49:16.265892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-12-26T14:49:26.080567image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
AgeBMIBloodPressureDiabetesPedigreeFunctionGlucoseIdInsulinOutcomePregnanciesSkinThickness
Age1.0000.1250.3500.0390.274-0.008-0.1210.3300.601-0.058
BMI0.1251.0000.2880.1390.2280.0180.2120.305-0.0050.462
BloodPressure0.3500.2881.0000.0290.2230.010-0.0070.1880.1800.142
DiabetesPedigreeFunction0.0390.1390.0291.0000.077-0.0030.2290.186-0.0410.177
Glucose0.2740.2280.2230.0771.0000.0130.2200.4900.1190.069
Id-0.0080.0180.010-0.0030.0131.0000.0150.044-0.0230.015
Insulin-0.1210.212-0.0070.2290.2200.0151.0000.172-0.1320.536
Outcome0.3300.3050.1880.1860.4900.0440.1721.0000.2490.202
Pregnancies0.601-0.0050.180-0.0410.119-0.023-0.1320.2491.000-0.076
SkinThickness-0.0580.4620.1420.1770.0690.0150.5360.202-0.0761.000

Missing values

2024-12-26T14:49:18.607882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-26T14:49:18.977407image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
0161487235033.60.627501
121856629026.60.351310
238183640023.30.672321
3418966239428.10.167210
450137403516843.12.288331
565116740025.60.201300
6737850328831.00.248261
781011500035.30.134290
892197704554330.50.158531
910812596000.00.232541
IdPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
27582759311190127828.40.495290
275927606102820030.80.180361
276027616134702313035.40.542291
27612762287023028.90.773250
2762276317960424843.50.678230
2763276427564245529.70.370330
276427658179724213032.70.719361
27652766685780031.20.382420
2766276701291104613067.10.319261
2767276828172157630.10.547250